Author
Yu-Che Chen
University of Nebraska at Omaha
Biography
Yu-Che Chen, PhD, is director of and professor at the School of Public Administration and director of the Digital Governance and Analytics lab at the University of Nebraska at Omaha. He serves as president of the global Digital Government Society (DGS). He is also a fellow of the National Academy of Public Administration in the US and a senior fellow at the Center for Asia-Pacific Resilience and Innovation (CAPRI). His expertise lies in the governance and policy of artificial intelligence and digital technologies, focusing on creating public value. Dr. Chen has served as the PI or co-PI of research grants totaling over $3.7 million and has authored four books and over 50 peer-reviewed publications on digital government and governance. He received his PhD in public policy from Indiana University, Bloomington.
Holding artificial intelligence (AI) accountable is challenging because machine learning, which is central to AI development, is complex and opaque. These characteristics of AI not only present significant obstacles for policymakers and citizens alike to understand how AI works but also create governance challenges in ensuring transparency and accountability in a democratic society.[i]
The rapid development and increasing capabilities of AI pose elevated societal risks, such as displacing the existing workforce and restructuring the economy.[ii] Furthermore, deepfakes and misinformation can erode the integrity of democratic elections.[iii] AI use also raises concerns about consumer protection and privacy.
This article addresses these AI-related societal challenges. It first identifies the main forces shaping AI governance, namely, the race for national economic competitiveness and the interplay between big tech firms and governments. Within the context of these macro-level forces, AI governance solutions and innovations are introduced. These include regional and global governance schemes, a comprehensive regulatory framework, and capacity building.
Forces shaping AI governance
National economic competitiveness in the AI race
Economic impact and competitiveness are shaping AI policy priorities and governance options at the national level. The US is a case in point: The market valuation and AI capital investment of big corporations such as Nvidia, Meta, Microsoft, and Google provide a robust economic base for US national competitiveness built on a strong AI industry. The current US administration is advancing the goal of maintaining global AI leadership through executive orders and other strategies.[iv] Furthermore, the US AI Action Plan, unveiled in July 2025, provides a roadmap for executing this vision. These actions prioritize national economic competitiveness over other societal concerns and favor removing regulatory barriers to AI investment.
International competition for leadership in the AI industry influences policy and governance. The most salient is the US–China race to develop AI products and services and secure a global market. The US has used the US Chips and Science Act to regulate the types of AI chips that can be exported to China.[v] Chinese technology and AI companies are becoming more competitive, especially in the open-source space and in developing countries.[vi] Such economic competition justifies national governments in prioritizing economic development over other societal concerns.
Interplay between big tech corporations and governments
Large technology corporations are exerting significant influence on national AI policy and regulations. In the US, AI companies and major technological corporations with significant AI investments have been advocating for self-regulation to avoid becoming subject to a comprehensive national AI regulatory framework. In addition, major domestic AI companies could solicit government financial support on the one hand and lobby for the removal of regulatory barriers to enhance economic competitiveness on the other.[vii] Consequently, governments are unlikely to develop and enforce a rigorous and comprehensive regulatory framework for AI development and use.
Nevertheless, governments have policy tools to influence big tech corporations. The European Union’s AI Act provides a comprehensive risk-based framework for AI products and services, with substantial financial implications for big tech companies selling AI products and services in the EU. Compliance with these regulations adds to a firm’s costs of developing and delivering AI products and services. Governments can also assert their interest over big tech by advancing sovereign AI—that is, independence in the control of AI systems based on national interests, values, and laws. Governments can negotiate business terms with AI companies by citing national security reasons. For instance, the EU’s digital sovereignty initiatives can strengthen regional governments’ regulatory position over big tech.[viii]
Given the race for global AI dominance and the interplay between government and big tech, AI governance options need to be negotiated with an overarching focus on economic competitiveness. Moreover, the promotion of public values depends on a dynamic balance between public and corporate interests.
Approaches and innovations in AI governance
A global or regional approach
A regional or even global approach can help effectively govern AI products and services produced by big tech. Global organizations and summits, such as the World Economic Forum’s AI Governance Alliance and ITU/UN’s AI for Good Global Summit, can help identify global policy priorities and cross-border initiatives.[ix] The EU AI Act and its ensuing implementation exemplify the need for a regional AI governance approach that aligns with the values and preferences of EU member countries. Such a regional approach can harmonize technical standards for AI risk management, providing corporations with access to a regional market and reducing compliance costs across borders, and safeguard against high-risk AI products and services.
A global self-regulation scheme for AI products and services can refer to or build upon a worldwide standard with independent certification, similar to the ISO certification process, in which ISO/IEC 42001:2023 was established specifically for AI systems, but adapted to address AI product safety and trustworthiness.[x] The publication of safety ratings for cars, as a market signal for product safety features valued by customers, could serve as a template to leverage market forces to incentivize companies to prioritize safety in their AI products and services.[xi] Moreover, a global rating scheme for AI transparency led by global institutions can drive government investment in transparent and trustworthy AI and its impact. For example, the United Nations’ E-Government Rankings incentivize countries’ investment in enhancing e-government services.
A comprehensive AI governance framework
A comprehensive AI framework is beneficial for managing the risks of AI products and services as well as integrating regulation, strategy, and implementation. A rigorous regulatory framework with effective enforcement provides guidelines and requirements, as well as financial incentives, to ensure the safety, trustworthiness, and accountability of AI. Unlike the approach taken by the US, the EU AI Act employs a tiered risk-based model to comprehensively classify and regulate AI products and services. Taiwan’s AI Basic Act outlines AI governance principles, designates the responsible agency, and prescribes an overarching strategy.[xii] Similarly, Korea’s AI Basic Act extends beyond regulation to integrate strategy, promotion, and regulation into a single legislation.[xiii]
These frameworks can further grow in sophistication as policymakers understand how and what public values are introduced, codified, implemented, and influenced throughout the AI lifecycle.[xiv] They can also benefit from the adaptive design of rules and regulations as well as various governance mechanisms, such as a steering board at the national or international level, to monitor and respond to rapidly evolving AI capabilities.[xv]
Capacity building through public infrastructure, civic literacy, and government training
Publicly supported AI infrastructure can help narrow the AI resource gap between big tech and the rest of society, including academic researchers, small and medium-sized enterprises, and entrepreneurs. Moreover, the democratization of AI infrastructure will provide a healthy and diversified ecosystem of AI products and services and a rich array of policy and regulatory perspectives beyond those of big tech. For example, in the US, the National Science Foundation provides free advanced computing resources to academic researchers for computationally intensive research, including those developing or using AI.[xvi] At the state level, California initiated CalCompute in 2025 to provide free or low-cost AI computing resources for researchers and entrepreneurs.[xvii]
Civic AI literacy can help overcome the democratic challenges posed by the opacity of machine learning and the knowledge gap between AI developers and users, thereby facilitating effective participatory AI governance. Furthermore, more inclusive AI can create public value.[xviii] Educational institutions can play a significant role here by providing independent expert knowledge and advice, such as Stanford University’s Institute for Human-Centered Artificial Intelligence.[xix]
Government training programs are also crucial for supporting public value–focused AI development and implementation for public services. AI capacity building among civil servants can also ensure that AI serves the public interest, as seen in Taiwan’s government-wide comprehensive training program for AI use and governance. The governments of Canada, Singapore, and the US also provide AI training programs targeting public services and values, thereby enhancing public servants’ capacity to enact good AI governance.
Conclusion
The rapid development of AI presents significant governance challenges while also offering opportunities to create public value for national and global communities. AI governance is influenced by the race for global dominance in the AI industry, which prioritizes economic competitiveness over concerns about ethics, safety, and civil liberty, as well as the interplay between government and big tech, which dynamically determines the type and degree of AI regulation. Within this context, three governance strategies can strengthen the public values of safety, trustworthiness, equity, efficiency, and effectiveness. First, a global or regional approach to AI governance to capitalize on the economic benefits and policy effectiveness of harmonizing standards and policy priorities. Second, a comprehensive AI governance framework can better manage the variety and degrees of risk and integrate strategies and regulations to create public value effectively. Finally, the investment in public computing infrastructure, the cultivation of civic AI literacy, and the training of public servants can work in tandem to advance the public interest in AI governance.
[i] Theodore M Lechterman, “The Concept of Accountability in AI Ethics and Governance,” in The Oxford Handbook of AI Governance, ed. Justin B. Bullock et al. (Oxford University Press, 2024), https://doi.org/10.1093/oxfordhb/9780197579329.013.10.
[ii] For a more extensive and future-oriented discussion, please refer to Ahmed et al., “Impact of AI-Generated Misinformation” and “Don’t blame AI for your job woes,” The Economist,November 6, 2025, https://www.economist.com/finance-and-economics/2025/11/06/dont-blame-ai-for-your-job-woes.
[iii] Saquib Ahmed et al., “Impact of AI-Generated Misinformation on Electoral Integrity and Public Trust,” in Democracy and Democratization in the Age of AI, ed. Kittisak Wongmahesak et al. (IGI Global Scientific Publishing, 2025), https://doi.org/10.4018/979-8-3693-8749-8.ch004.
[iv] EOs 13859, 14141, and 14179 have explicit reference to AI leadership in a competitive context.
[v] Alexandra Alper et al., “Biden Cuts China off from More Nvidia Chips, Expands Curbs to Other Countries.” Technology, Reuters, October 17, 2023, https://www.reuters.com/technology/biden-cut-china-off-more-nvidia-chips-expand-curbs-more-countries-2023-10-17/.
[vi] “China Is Quietly Upstaging America with Its Open Models,” The Economist, August 21, 2025, https://www.economist.com/business/2025/08/21/china-is-quietly-upstaging-america-with-its-open-models.
[vii] Tyler Katzenberger and Christine Mui, “Meta to Launch California Super PAC Focused on AI.” POLITICO, August 26, 2025, https://www.politico.com/news/2025/08/26/exclusive-meta-to-launch-california-super-pac-focused-on-ai-00524989.
[viii] Digital Hub Denmark, “Decoding: Europe Can’t Regulate Its Way to Digital Sovereignty. It Must Build It,” June 2025, https://www.digitalhubdenmark.dk/post/decoding-4.
[ix] “Design of Transparent and Inclusive AI Systems – AI Governance Alliance,” Accessed November 17, 2025, https://initiatives.weforum.org/ai-governance-alliance/home.
[x] This standard provides a formal framework for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS) within organizations, promoting responsible, ethical, and transparent use of AI technologies.
[xi] Archon Fung et al., Full Disclosure: The Perils and Promise of Transparency (Cambridge University Press, 2007).
[xii] Keoni Everington, “Taiwan passes AI Basic Act,” Taiwan News, December 23, 2025, https://taiwannews.com.tw/news/6270744
[xiii] Ministry of Science and ICT, “A New Chapter in the Age of AI: Basic Act on AI Passed at the National Assembly’s Plenary Session.” December 26, 2024, https://www.msit.go.kr/eng/bbs/view.do?sCode=eng&mId=4&mPid=2&pageIndex=&bbsSeqNo=42&nttSeqNo=1071&searchOpt=ALL&searchTxt=; S. Kim and H. Omar, “The light and shade of an integrated approach: The structure and regulatory risks of Korea’s AI Basic Act,” Information Technology and Innovation Foundation (ITIF), September 2025, https://www2.itif.org/2025-korea-ai-act-ko.pdf.
[xiv] Yu-Che Chen and Michael Ahn, “Governing AI Systems for Public Values: Design Principles and a Process Framework,” in The Oxford Handbook of AI Governance, https://doi.org/10.1093/oxfordhb/9780197579329.013.31.
[xv] A review of AI governance mechanisms and a set of governance recommendations are available in Yu-Che Chen et al., “Artificial Intelligence and Public Values: Value Impacts and Governance in the Public Sector,” Sustainability 15 no. 6 (2023): 4796, https://doi.org/10.3390/su15064796.
[xvi] Access, “Home,” accessed November 12, 2025, https://access-ci.org/.
[xvii] Teri Olle, “California Advances AI Safety and Public Cloud Access,” Economic Security Project Action, July 9, 2025, https://economicsecurity.us/news/california-advances-ai-safety-and-public-cloud-access/.
[xviii] M. Jae Moon, “Searching for Inclusive Artificial Intelligence for Social Good: Participatory Governance and Policy Recommendations for Making AI More Inclusive and Benign for Society,” Public Administration Review 83, no. 6 (2023): 1496–505. https://doi.org/10.1111/puar.13648.
[xix] Stanford HAI, “Policy,” Accessed November 17, 2025, https://hai.stanford.edu/policy.
Global Innovation Reimagined
Global Innovation Reimagined showcases reflections and research on innovation in its many forms across Asia, North America, and Europe. The perspectives offered herein draw from discussions during the trilateral Reimagining Entrepreneurship and Innovation conference, hosted by CAPRI, CAPRI USA, the University of Virginia, and Copenhagen Business School from July 22 to 25, 2025.
Forces and Innovations in AI Governance for Public Value Creation
Author
Yu-Che Chen
University of Nebraska at Omaha
Biography
Yu-Che Chen, PhD, is director of and professor at the School of Public Administration and director of the Digital Governance and Analytics lab at the University of Nebraska at Omaha. He serves as president of the global Digital Government Society (DGS). He is also a fellow of the National Academy of Public Administration in the US and a senior fellow at the Center for Asia-Pacific Resilience and Innovation (CAPRI). His expertise lies in the governance and policy of artificial intelligence and digital technologies, focusing on creating public value. Dr. Chen has served as the PI or co-PI of research grants totaling over $3.7 million and has authored four books and over 50 peer-reviewed publications on digital government and governance. He received his PhD in public policy from Indiana University, Bloomington.
Holding artificial intelligence (AI) accountable is challenging because machine learning, which is central to AI development, is complex and opaque. These characteristics of AI not only present significant obstacles for policymakers and citizens alike to understand how AI works but also create governance challenges in ensuring transparency and accountability in a democratic society.[i]
The rapid development and increasing capabilities of AI pose elevated societal risks, such as displacing the existing workforce and restructuring the economy.[ii] Furthermore, deepfakes and misinformation can erode the integrity of democratic elections.[iii] AI use also raises concerns about consumer protection and privacy.
This article addresses these AI-related societal challenges. It first identifies the main forces shaping AI governance, namely, the race for national economic competitiveness and the interplay between big tech firms and governments. Within the context of these macro-level forces, AI governance solutions and innovations are introduced. These include regional and global governance schemes, a comprehensive regulatory framework, and capacity building.
Forces shaping AI governance
National economic competitiveness in the AI race
Economic impact and competitiveness are shaping AI policy priorities and governance options at the national level. The US is a case in point: The market valuation and AI capital investment of big corporations such as Nvidia, Meta, Microsoft, and Google provide a robust economic base for US national competitiveness built on a strong AI industry. The current US administration is advancing the goal of maintaining global AI leadership through executive orders and other strategies.[iv] Furthermore, the US AI Action Plan, unveiled in July 2025, provides a roadmap for executing this vision. These actions prioritize national economic competitiveness over other societal concerns and favor removing regulatory barriers to AI investment.
International competition for leadership in the AI industry influences policy and governance. The most salient is the US–China race to develop AI products and services and secure a global market. The US has used the US Chips and Science Act to regulate the types of AI chips that can be exported to China.[v] Chinese technology and AI companies are becoming more competitive, especially in the open-source space and in developing countries.[vi] Such economic competition justifies national governments in prioritizing economic development over other societal concerns.
Interplay between big tech corporations and governments
Large technology corporations are exerting significant influence on national AI policy and regulations. In the US, AI companies and major technological corporations with significant AI investments have been advocating for self-regulation to avoid becoming subject to a comprehensive national AI regulatory framework. In addition, major domestic AI companies could solicit government financial support on the one hand and lobby for the removal of regulatory barriers to enhance economic competitiveness on the other.[vii] Consequently, governments are unlikely to develop and enforce a rigorous and comprehensive regulatory framework for AI development and use.
Nevertheless, governments have policy tools to influence big tech corporations. The European Union’s AI Act provides a comprehensive risk-based framework for AI products and services, with substantial financial implications for big tech companies selling AI products and services in the EU. Compliance with these regulations adds to a firm’s costs of developing and delivering AI products and services. Governments can also assert their interest over big tech by advancing sovereign AI—that is, independence in the control of AI systems based on national interests, values, and laws. Governments can negotiate business terms with AI companies by citing national security reasons. For instance, the EU’s digital sovereignty initiatives can strengthen regional governments’ regulatory position over big tech.[viii]
Given the race for global AI dominance and the interplay between government and big tech, AI governance options need to be negotiated with an overarching focus on economic competitiveness. Moreover, the promotion of public values depends on a dynamic balance between public and corporate interests.
Approaches and innovations in AI governance
A global or regional approach
A regional or even global approach can help effectively govern AI products and services produced by big tech. Global organizations and summits, such as the World Economic Forum’s AI Governance Alliance and ITU/UN’s AI for Good Global Summit, can help identify global policy priorities and cross-border initiatives.[ix] The EU AI Act and its ensuing implementation exemplify the need for a regional AI governance approach that aligns with the values and preferences of EU member countries. Such a regional approach can harmonize technical standards for AI risk management, providing corporations with access to a regional market and reducing compliance costs across borders, and safeguard against high-risk AI products and services.
A global self-regulation scheme for AI products and services can refer to or build upon a worldwide standard with independent certification, similar to the ISO certification process, in which ISO/IEC 42001:2023 was established specifically for AI systems, but adapted to address AI product safety and trustworthiness.[x] The publication of safety ratings for cars, as a market signal for product safety features valued by customers, could serve as a template to leverage market forces to incentivize companies to prioritize safety in their AI products and services.[xi] Moreover, a global rating scheme for AI transparency led by global institutions can drive government investment in transparent and trustworthy AI and its impact. For example, the United Nations’ E-Government Rankings incentivize countries’ investment in enhancing e-government services.
A comprehensive AI governance framework
A comprehensive AI framework is beneficial for managing the risks of AI products and services as well as integrating regulation, strategy, and implementation. A rigorous regulatory framework with effective enforcement provides guidelines and requirements, as well as financial incentives, to ensure the safety, trustworthiness, and accountability of AI. Unlike the approach taken by the US, the EU AI Act employs a tiered risk-based model to comprehensively classify and regulate AI products and services. Taiwan’s AI Basic Act outlines AI governance principles, designates the responsible agency, and prescribes an overarching strategy.[xii] Similarly, Korea’s AI Basic Act extends beyond regulation to integrate strategy, promotion, and regulation into a single legislation.[xiii]
These frameworks can further grow in sophistication as policymakers understand how and what public values are introduced, codified, implemented, and influenced throughout the AI lifecycle.[xiv] They can also benefit from the adaptive design of rules and regulations as well as various governance mechanisms, such as a steering board at the national or international level, to monitor and respond to rapidly evolving AI capabilities.[xv]
Capacity building through public infrastructure, civic literacy, and government training
Publicly supported AI infrastructure can help narrow the AI resource gap between big tech and the rest of society, including academic researchers, small and medium-sized enterprises, and entrepreneurs. Moreover, the democratization of AI infrastructure will provide a healthy and diversified ecosystem of AI products and services and a rich array of policy and regulatory perspectives beyond those of big tech. For example, in the US, the National Science Foundation provides free advanced computing resources to academic researchers for computationally intensive research, including those developing or using AI.[xvi] At the state level, California initiated CalCompute in 2025 to provide free or low-cost AI computing resources for researchers and entrepreneurs.[xvii]
Civic AI literacy can help overcome the democratic challenges posed by the opacity of machine learning and the knowledge gap between AI developers and users, thereby facilitating effective participatory AI governance. Furthermore, more inclusive AI can create public value.[xviii] Educational institutions can play a significant role here by providing independent expert knowledge and advice, such as Stanford University’s Institute for Human-Centered Artificial Intelligence.[xix]
Government training programs are also crucial for supporting public value–focused AI development and implementation for public services. AI capacity building among civil servants can also ensure that AI serves the public interest, as seen in Taiwan’s government-wide comprehensive training program for AI use and governance. The governments of Canada, Singapore, and the US also provide AI training programs targeting public services and values, thereby enhancing public servants’ capacity to enact good AI governance.
Conclusion
The rapid development of AI presents significant governance challenges while also offering opportunities to create public value for national and global communities. AI governance is influenced by the race for global dominance in the AI industry, which prioritizes economic competitiveness over concerns about ethics, safety, and civil liberty, as well as the interplay between government and big tech, which dynamically determines the type and degree of AI regulation. Within this context, three governance strategies can strengthen the public values of safety, trustworthiness, equity, efficiency, and effectiveness. First, a global or regional approach to AI governance to capitalize on the economic benefits and policy effectiveness of harmonizing standards and policy priorities. Second, a comprehensive AI governance framework can better manage the variety and degrees of risk and integrate strategies and regulations to create public value effectively. Finally, the investment in public computing infrastructure, the cultivation of civic AI literacy, and the training of public servants can work in tandem to advance the public interest in AI governance.
[i] Theodore M Lechterman, “The Concept of Accountability in AI Ethics and Governance,” in The Oxford Handbook of AI Governance, ed. Justin B. Bullock et al. (Oxford University Press, 2024), https://doi.org/10.1093/oxfordhb/9780197579329.013.10.
[ii] For a more extensive and future-oriented discussion, please refer to Ahmed et al., “Impact of AI-Generated Misinformation” and “Don’t blame AI for your job woes,” The Economist,November 6, 2025, https://www.economist.com/finance-and-economics/2025/11/06/dont-blame-ai-for-your-job-woes.
[iii] Saquib Ahmed et al., “Impact of AI-Generated Misinformation on Electoral Integrity and Public Trust,” in Democracy and Democratization in the Age of AI, ed. Kittisak Wongmahesak et al. (IGI Global Scientific Publishing, 2025), https://doi.org/10.4018/979-8-3693-8749-8.ch004.
[iv] EOs 13859, 14141, and 14179 have explicit reference to AI leadership in a competitive context.
[v] Alexandra Alper et al., “Biden Cuts China off from More Nvidia Chips, Expands Curbs to Other Countries.” Technology, Reuters, October 17, 2023, https://www.reuters.com/technology/biden-cut-china-off-more-nvidia-chips-expand-curbs-more-countries-2023-10-17/.
[vi] “China Is Quietly Upstaging America with Its Open Models,” The Economist, August 21, 2025, https://www.economist.com/business/2025/08/21/china-is-quietly-upstaging-america-with-its-open-models.
[vii] Tyler Katzenberger and Christine Mui, “Meta to Launch California Super PAC Focused on AI.” POLITICO, August 26, 2025, https://www.politico.com/news/2025/08/26/exclusive-meta-to-launch-california-super-pac-focused-on-ai-00524989.
[viii] Digital Hub Denmark, “Decoding: Europe Can’t Regulate Its Way to Digital Sovereignty. It Must Build It,” June 2025, https://www.digitalhubdenmark.dk/post/decoding-4.
[ix] “Design of Transparent and Inclusive AI Systems – AI Governance Alliance,” Accessed November 17, 2025, https://initiatives.weforum.org/ai-governance-alliance/home.
[x] This standard provides a formal framework for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS) within organizations, promoting responsible, ethical, and transparent use of AI technologies.
[xi] Archon Fung et al., Full Disclosure: The Perils and Promise of Transparency (Cambridge University Press, 2007).
[xii] Keoni Everington, “Taiwan passes AI Basic Act,” Taiwan News, December 23, 2025, https://taiwannews.com.tw/news/6270744
[xiii] Ministry of Science and ICT, “A New Chapter in the Age of AI: Basic Act on AI Passed at the National Assembly’s Plenary Session.” December 26, 2024, https://www.msit.go.kr/eng/bbs/view.do?sCode=eng&mId=4&mPid=2&pageIndex=&bbsSeqNo=42&nttSeqNo=1071&searchOpt=ALL&searchTxt=; S. Kim and H. Omar, “The light and shade of an integrated approach: The structure and regulatory risks of Korea’s AI Basic Act,” Information Technology and Innovation Foundation (ITIF), September 2025, https://www2.itif.org/2025-korea-ai-act-ko.pdf.
[xiv] Yu-Che Chen and Michael Ahn, “Governing AI Systems for Public Values: Design Principles and a Process Framework,” in The Oxford Handbook of AI Governance, https://doi.org/10.1093/oxfordhb/9780197579329.013.31.
[xv] A review of AI governance mechanisms and a set of governance recommendations are available in Yu-Che Chen et al., “Artificial Intelligence and Public Values: Value Impacts and Governance in the Public Sector,” Sustainability 15 no. 6 (2023): 4796, https://doi.org/10.3390/su15064796.
[xvi] Access, “Home,” accessed November 12, 2025, https://access-ci.org/.
[xvii] Teri Olle, “California Advances AI Safety and Public Cloud Access,” Economic Security Project Action, July 9, 2025, https://economicsecurity.us/news/california-advances-ai-safety-and-public-cloud-access/.
[xviii] M. Jae Moon, “Searching for Inclusive Artificial Intelligence for Social Good: Participatory Governance and Policy Recommendations for Making AI More Inclusive and Benign for Society,” Public Administration Review 83, no. 6 (2023): 1496–505. https://doi.org/10.1111/puar.13648.
[xix] Stanford HAI, “Policy,” Accessed November 17, 2025, https://hai.stanford.edu/policy.
Global Innovation Reimagined
Global Innovation Reimagined showcases reflections and research on innovation in its many forms across Asia, North America, and Europe. The perspectives offered herein draw from discussions during the trilateral Reimagining Entrepreneurship and Innovation conference, hosted by CAPRI, CAPRI USA, the University of Virginia, and Copenhagen Business School from July 22 to 25, 2025.
About the Author
Yu-Che Chen
More
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